COMBINED LMS F ALGORITHM

A new adaptive filter algorithm has been developed that combines the b enefits of the least mean square (LMS) and least mean fourth (LMF) met hods. This algorithm, called LMS/F, outperforms the standard LMS algor ithm judging either constant convergence rate or constant misadjustmen t. While LMF outperforms LMS for certain noise profiles, its stability cannot be guaranteed for known input signals even for very small step sizes. However, both LMS and LMS/F have good stability properties and LMS/F only adds a few more computations per iteration compared to LMS . Simulations of a non-stationary system identification problem demons trate the performance benefits of the LMS/F algorithm.